Misinformation in online spaces can stoke mistrust of established media, misinform the public and lead to radicalization. Hence, multiple automated algorithms for misinformation detection have been proposed in the recent past. However, the fairness (e.g., performance across left- and right- leaning news articles) of these algorithms has been repeatedly questioned, leading to decreased trust in such systems. This work motivates and grounds the need for an audit of machine learning based misinformation detection algorithms and possible ways to mitigate bias (if found). Using a large (N>100K) corpus of news articles, we report that multiple standard machine learning based misinformation detection approaches are susceptible to bias. Further, we find that an intuitive post-processing approach (Reject Option Classifier) can reduce bias while maintaining high accuracy in the above setting. The results pave the way for accurate yet fair misinformation detection algorithms.
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This content will become publicly available on April 30, 2024
Misinformation Detection Algorithms and Fairness across Political Ideologies: The Impact of Article Level Labeling
Multiple recent efforts have used large-scale data and computational models to automatically detect misinformation in online news articles. Given the potential impact of misinformation on democracy, many of these efforts have also used the political ideology of these articles to better model misinformation and study political bias in such algorithms. However, almost all such efforts have used source level labels for credibility and political alignment, thereby assigning the same credibility and political alignment label to all articles from the same source (e.g., the New York Times or Breitbart). Here, we report on the impact of journalistic best practices to label individual news articles for their credibility and political alignment. We found that while source level labels are decent proxies for political alignment labeling, they are very poor proxies-almost the same as flipping a coin-for credibility ratings. Next, we study the implications of such source level labeling on downstream processes such as the development of automated misinformation detection algorithms and political fairness audits therein. We find that the automated misinformation detection and fairness algorithms can be suitably revised to support their intended goals but might require different assumptions and methods than those which are appropriate using source level labeling. The results suggest caution in generalizing recent results on misinformation detection and political bias therein. On a positive note, this work shares a new dataset of journalistic quality individually labeled articles and an approach for misinformation detection and fairness audits.
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- Award ID(s):
- 1915790
- NSF-PAR ID:
- 10438812
- Date Published:
- Journal Name:
- Proceedings of the 15th ACM Web Science Conference 2023
- Page Range / eLocation ID:
- 107 to 116
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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